Category Archives: Letters to Regulators

Letters to Regulators: Letter Commenting on HUD’s Mortgage Letter 2021-18

AFREF joined a letter to comment on HUD’s response to the COVID-19 pandemic through Mortgage Letter 2021-18. The letter calls on HUD to take further steps to ensure that its program operates as intended and that borrowers are able to obtain the relief they need to avoid unnecessary foreclosures.

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Letters to Regulators: Letter to FHFA on Duty to Serve

AFREF and MHAction submitted joint comments on Fannie Mae and Freddie Mac’s duty to serve plans and urged the Enterprises to better protect manufactured housing residents from corporate abuses and increase financing opportunities for mission driven owners.

Federal reserve board

Letters to Regulators: Letter to the Federal Reserve on Access to Fed Master Accounts and Financial Services

AFREF submitted a comment to the Federal Reserve on its “Proposed Guidelines for Evaluating Account and Service Requests” urging the Federal Reserve to limit access to its payments systems to well-regulated and well-supervised depository institutions to safeguard rules that protect consumers, ensure the stability of the payments system, and require community reinvestment.

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Letters to Regulators: Letter to OMB in Response to Racial Equity RFI

AFREF joined a letter to the Office of Management and Budget in response to their RFI asking for input on how to identify effective methods for assessing whether agency policies, programs, services, processes, and operations equitably serve all eligible individuals and communities, particularly those that are currently and historically underserved.

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Letters to Regulators: Letter Urging Financial Regulators to Consider Fair Lending Risks in Use of Artificial Intelligence

AFREF and 22 organizations submitted comments in response to the regulators’ Request for Information and Comment on Financial Institutions’ Use of Artificial Intelligence, including Machine Learning, urging the financial regulators to consider fair lending risks of using artificial intelligence and machine learning and enact safeguards to prevent disproportionate adverse impacts from the use of AI/ML models.